Changes in efficiency of tertiary public general hospitals during the reform of public hospitals in Beijing, China

Zhu, Junli; Song, Xinrui

doi:10.1002/hpm.3309

Cited by 10 publications

(10 citation statements)

References 20 publications

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“…Among the studies, 80 (89.89%) conducted extended analysis in their research [46–56,58–64,66–69,71–77,79–94,96–98,101–116,118–125,128–135]. The type of data applied were almost equally distributed among the studies 51 (57.30%) [47,49,52,58–60,67,70–73,75–77,79,80,83–85,88,91,92,94,96,97,100,101,103,105–109,111,112,114–116,120–122,124,125,127,129–135] used panel data and 38 (42.70%) [46,48,50,51,53–57,61–66,68,69,74,78,81,82,86,87,89,90,93,95,98,99,102,104,110,113,117–119,123,128] used cross sectional data in their research. Forty extended analyses were identified within the included studies.…”

Section: Resultsmentioning

confidence: 99%

“…Researchers has been incorporating systematic procedure to judge this process. In the review, four systematic approaches have been identified which are Delphi method, Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE), Bibliometric analysis, and Variance filter [49,58,61,102,109]. The Delphi method is a methodology used in research models that is centred on gathering the most trustworthy consensus of expert opinion for challenging situations.…”

Section: Systematic Methodsmentioning

confidence: 99%

“…While others detailed the model as Radial, Non-Radial and Oriented, Non-Radial and Non-Oriented, and Radial and Non-Radial [23,126]. The majority of the research that were included used Radial DEA models (72 of 89, 80.90%) [46,[48][49][50][51][52][54][55][56][57][58][59][60][61][62][65][66][67][68][69][70][71][72][73][74][75][76][77]79,80,[82][83][84][85][86][89][90][91][92][93][94][96][97][98][99][100][102][103]…”

Section: Model Typementioning

confidence: 99%

“…The dominant combination approach identified was literature review & data availability (13.48%, 12 of 89)[56,57,79,83,104,106,107,112,114,123,129,131], followed by literature review & systematic method (5.62%, 5 of 89)[58,61,84,102,109] and literature review & DMU limitation (5.62%, 5 of 89)[49,80,87,95,99]. The maximum combination was four, which one study mentioned in their article[88].The complete list of the specific approach presented in…”

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confidence: 99%

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Approach in Inputs & Outputs Selection of Data Envelopment Analysis (DEA) Efficiency Measurement in Hospital: A Systematic Review

Zubir,

Noor,

Rizal

et al. 2023

Preprint

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Data Envelopment Analysis (DEA) has been employed as a performance evaluation tool in the evaluation of efficiency and productivity in numerous fields. This includes hospitals in particular as well as the broad healthcare industry. This review examines 89 papers that discuss the use of DEA in hospitals, paying particular attention to approaches for choosing inputs and outputs as well as the most recent developments in DEA studies. English articles with empirical data from year 2014-2022 (Web of Science, Scopus, PubMed, ScienceDirect, Springer Link, and Google Scholar) were extracted based on PRISMA methodology. DEA Model parameters were specified based on previous studies and approaches were identified narratively. The approaches can be grouped into four: (1) Literature review, (2) Data availability, (3) Systematic method and (4) Expert judgement. The approaches were applied as one strategy either by itself or in combination with others. This review's emphasis on approaches used in hospital may constrain its conclusions. There might be another strategy or method used to select the input and output for a DEA study in a different area or strategies based on different viewpoints. The trend for DEA application were quite similar to previous studies. There is no evidence that one model fits all DEA model parameters better than another. Based on the reviewed literature, we offer some recommendations and methodological principles for DEA studies.

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Section: Resultsmentioning

confidence: 99%

Section: Systematic Methodsmentioning

confidence: 99%

Section: Model Typementioning

confidence: 99%

mentioning

confidence: 99%

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Approach in Inputs & Outputs Selection of Data Envelopment Analysis (DEA) Efficiency Measurement in Hospital: A Systematic Review

Zubir,

Noor,

Rizal

et al. 2023

Preprint

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“…Between 2015 and 2019, slack resources were consistently more severe in secondary hospitals than in tertiary hospitals. Similarly, Jiang et al and Zhu et al found that the efficiency of Beijing’s tertiary hospitals has gradually improved after the new medical reform, while secondary hospitals did not perform as well as expected 48 49. Secondary hospitals in China treat common and multiple diseases and receive referrals from primary care institutions, which are the intermediate links of graded care 50.…”

Section: Discussionmentioning

confidence: 99%

Impact of slack resources on healthcare costs in tertiary and secondary hospitals: a panel data study of public hospitals in Beijing from 2015 to 2019

Chen¹,

Song²,

Zhu³

2023

BMJ Open

Self Cite

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ObjectiveThis study aims to explore the relationship between slack resources and cost consumption index in tertiary and secondary hospitals and to provide targeted healthcare resource utilisation recommendations for tertiary and secondary hospital managers.DesignThis is a panel data study of 51 public hospitals in Beijing from 2015 to 2019.SettingTertiary and secondary public hospitals in Beijing. Data envelope analysis was used to calculate the slack resources. Regression models were used to explore the relationship between slack resources and healthcare costs.ParticipantsA total of 255 observations were collected from 33 tertiary hospitals and 18 secondary hospitals.Outcome measuresSlack resources and healthcare costs in tertiary and secondary public hospitals in Beijing from 2015 to 2019. Linear or curve relationship between slack resources and healthcare costs in tertiary and secondary hospitals.ResultsThe cost of healthcare in tertiary hospitals has always been higher than in secondary hospitals, and the slack resources in secondary hospitals have always been worse than in tertiary hospitals. For tertiary hospitals, the cubic coefficient of slack resources is significant (β=−12.914, p<0.01) and the R2of cubic regression is increased compared with linear and quadratic regression models, so there is a transposed S-shaped relationship between slack resources and cost consumption index. For secondary hospitals, only the first-order coefficient of slack resources in the linear regression was significant (β=0.179, p<0.05), so slack resources in secondary hospitals were positively related to the cost consumption index.ConclusionsThis study shows that slack resources’ impact on healthcare costs differs in tertiary and secondary public hospitals. For tertiary hospitals, slack should be kept within a reasonable range to control excessive growth in healthcare costs. In secondary hospitals, keeping too many slack resources is not ideal, so managers should adopt strategies to improve competitiveness and service transformation.

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Can public hospital reform reduce medical resource mismatches? Evidence from China

Chai,

Liu,

et al. 2024

Health Econ Rev

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Background The mismatch of medical resources is a significant issue in global healthcare, undermining both service accessibility and system efficiency. In China, despite the implementation of the “Healthy China” strategy, persistent mismatches remain due to factors such as industrialization, urbanization, and population aging. This study empirically evaluates the impact of Public Hospital Reform (PHR) on mitigating these mismatches. Methods A Difference-in-Differences (DD) approach is applied to panel data from 300 cities spanning 2010 to 2021, using the phased implementation of PHR as a quasi-natural experiment. This allows for a comparative analysis of changes in resource allocation between cities that adopted the reform and those that did not. Quantile regression assesses the effects of PHR across varying levels of resource mismatch, while mechanism tests investigate how PHR influences mismatches through cost reduction and supply expansion. Results PHR is found to reduce medical resource mismatches by 13.9%, primarily driven by cost reductions and increased resource supply. The effects are more pronounced at both lower and higher levels of mismatch, with a limited impact at mid-levels. Furthermore, the reform’s effectiveness diminishes as it is extended to more cities, suggesting a potential saturation effect. Conclusions This study demonstrates that PHR significantly alleviates medical resource mismatches in China. The findings underscore the need to focus on cost control and resource supply in future healthcare reforms, providing key insights for policymakers in developing countries facing similar healthcare resource challenges. Supplementary Information The online version contains supplementary material available at 10.1186/s13561-024-00567-7.

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Changes in efficiency of tertiary public general hospitals during the reform of public hospitals in Beijing, China

Cited by 10 publications

References 20 publications

Approach in Inputs & Outputs Selection of Data Envelopment Analysis (DEA) Efficiency Measurement in Hospital: A Systematic Review

Approach in Inputs & Outputs Selection of Data Envelopment Analysis (DEA) Efficiency Measurement in Hospital: A Systematic Review

Impact of slack resources on healthcare costs in tertiary and secondary hospitals: a panel data study of public hospitals in Beijing from 2015 to 2019

Can public hospital reform reduce medical resource mismatches? Evidence from China

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